The present subject matter relates generally to asset management and more particularly to the monitoring of a fleet of mobile workstations, battery use and health, and providing an effective user interface for tracking the use and health of the fleet.
In the current state of the art, hospitals and other health care providers use a variety of different assets to streamline and supplement the care that they provide to patients. However, the number of devices and the use of those devices is inherently difficult to track, including replacement dates and allocation across a facility. Devices may be spread and moved across a large facility, making it difficult to track location and keep an accurate inventory of the devices. Furthermore, the device management may be difficult as certain devices may need to be decommissioned or replaced after a certain time period.
Battery health representations include tabular displays of battery charge or battery health. Moreover, traditional battery capacity measurements calculate only the charge capacity of the initial charge, and do not adapt to subsequent usage or charging patterns. More particularly, a battery will have a different charge capacity during its first use and several subsequent uses as the charge capacity decreases over time. The battery is generally still healthy during the first several uses even though the charge capacity is decreasing. Many batteries are measured for health in comparison to the initial charge capacity.
Another inherent difficulty includes allocating the resources to the right people and departments without having unused devices or a deficiency of those devices in the facility. Use of medical carts or similar devices in hospitals is not distributed evenly across the 24-hour day, rendering traditional (average) measures of use ineffective. Many of these devices are critical to patient care, and must be available and locatable at any time.
Additionally, battery-powered devices in hospital settings frequently result in multiple failures or can repeatedly fail during use. These failures generally go unreported by clinicians, creating a backlog of non-functional units until there are sufficient failures within the fleet of devices as to negatively impact patient care.
Further problems in the present state of the art exist in the stated full-charge capacity of the battery. Specifically, present measurements of “full-charge” capacities are static, such that the capacity of a battery is rated at point-of-manufacture, point-of-sale, or point-of-first-use, and is not subsequently updated despite usage of the battery.
Previous attempts to monitor battery health and predict future battery outcomes have relied upon limited amounts of data (often taken from the battery's initial stages) or rely on direct measurements, such as voltage or temperatures outside of a predetermined range or specification. However, no solutions currently exist which base need for battery service on the use patterns of a fleet of devices to determine when a user can or should switch to another similar device.
Accordingly, there exists a need for a battery and workstation monitoring system which can manage, predict, and display the asset health of a plurality of devices within and across a fleet of similar devices.